The Internet has provided a new way for people to connect and share information globally. Social media platforms, for example, enable people on opposite sides of the world to collaborate on ideas, discuss current events, or share what they had for lunch. In the past, this spectacular resource has been somewhat limited to communications between users having a common natural language (“language”). In addition, users have only been able to consume content that is in their language, or for which a content provider is able to provide an appropriate translation. While communication across different languages is a particular challenge, machine translation engines have been created to address this concern. These translation engines enable “content items,” which can be any item containing language including text, images, audio, video, or other multi-media, to be quickly translated for consumption by users that are facile with a language different from a source language of the content item.
Machine translation engines enable a user to select or provide a source content item (e.g., a message from an acquaintance) in one natural language (e.g., Spanish) and quickly receive a translation of the content item in a different natural language (e.g., English). Parts of machine translation engines can be created using training data that includes identical or similar content in two or more languages. Where machine translations are implemented on a large scale, determining which translations are sufficiently accurate poses a problem. For example, the word “lift” can mean “move upward” among speakers of American English (as that word is commonly used in America), whereas it can mean “elevator” for British English speakers. A content item including the phrase, “press the button for the lift,” could be translated into either “press the button for the elevator” or “press the button to go up.” In addition, machine translations of a content item are often based on dictionary translations and do not consider context, which often makes a significant difference such as in idioms, slang, or colloquial passages.
The techniques introduced here may be better understood by referring to the following Detailed Description in conjunction with the accompanying drawings, in which like reference numerals indicate identical or functionally similar elements.